Graphical user interface for call center analysis

ABSTRACT

One or more embodiments related to a method of generating a graphical user interface. The method includes obtaining a metric interface hierarchy having multiple nodes, where each node defines a visualization for the node, and the metric interface hierarchy defines an ordering on the nodes. The method further includes traversing the metric interface hierarchy starting with a selected node to obtain a subhierarchy, and creating the graphical user interface from a general interface by populating the general interface with the visualization from each node in the subhierarchy according to the ordering. The method further includes providing the graphical user interface.

BACKGROUND

A call center provides a centralized virtual and/or physical location tohandle communications with customers. In particular, when a customer hasa problem or question for a company, the customer may contact the callcenter and communicate with an employee who assists the person with theproblem or question. The employees of the call center are given variousroles, such as agents, supervisors, and directors. The agents of thecall center provide the first level of interaction with customers.Agents often follow scripts or are trained in a specific set of rules.Supervisors monitor and evaluate the agents. The call center directormanages the end to end operations of the call center.

Customers contact the call center through communication channels, suchas phone, chat, and other channels. At the call center side, thecommunication channels to agents are controlled by various devices. Thedevices manage the routing and connection of the employees of the callcenter to the customers.

BRIEF DESCRIPTION OF DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 7 illustrates a block diagram of an example cloud-based data intakeand query system in which an embodiment may be implemented;

FIG. 8 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 9 illustrates a block diagram of a system in accordance withdisclosed embodiments;

FIG. 10 illustrates a block diagram of a search head in accordance withdisclosed embodiments;

FIG. 11 is a flow diagram illustrating how an interaction metric may bedetermined in accordance with disclosed embodiments;

FIG. 12 is a flow diagram illustrating how to determine a currentinteraction metric on a per group basis in accordance with disclosedembodiments;

FIG. 13 is a flow diagram for processing a pipeline command to determinea current interaction metric in accordance with disclosed embodiments;

FIG. 14 is a flow diagram illustrating how to determine a number ofagents in accordance with disclosed embodiments;

FIG. 15 is a flow diagram illustrating how to correlate third party datawith call center information to determine a future interaction metric inaccordance with disclosed embodiments;

FIG. 16 is a flow diagram illustrating how to predict a future volume ofcalls in accordance with disclosed embodiments;

FIG. 17 is a flow diagram illustrating how to generate a graphical userinterface in accordance with disclosed embodiments;

FIG. 18 is a diagram of a general interface in accordance with disclosedembodiments; and

FIGS. 19-22 are example graphical user interfaces in accordance with thedisclosed embodiments.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. However, it will be apparent toone of ordinary skill in the art that the invention may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, third,etc.) may be used as an adjective for an element (i.e., any noun in theapplication). The use of ordinal numbers is not to imply or create anyparticular ordering of the elements nor to limit any element to beingonly a single element unless expressly disclosed, such as by the use ofthe terms “before”, “after”, “single”, and other such terminology.Rather, the use of ordinal numbers is to distinguish between theelements. By way of an example, a first element is distinct from asecond element, and the first element may encompass more than oneelement and succeed (or precede) the second element in an ordering ofelements.

Further, although the description includes a discussion of variousembodiments of the invention, the various disclosed embodiments may becombined in virtually any manner. All combinations are contemplatedherein.

In general, one or more embodiments are directed to determining acurrent interaction metric on a per group basis for the call center. Thecurrent interaction metric identifies how the call center is currentlyoperating. The current interaction metric may further provideinformation for optimizing the current state of the call center.

In one or more embodiments are further directed to generating agraphical user interface from a general interface. The graphical userinterface may be generated using a metric interface hierarchy havingmultiple ordered nodes. Each node defines a corresponding visualizationfor the node. The method for generating the graphical user interface maybe used to display the current interaction metric in accordance withdisclosed embodiments.

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

-   -   2.1. Host Devices    -   2.2. Client Devices    -   2.3. Client Device Applications    -   2.4. Data Server System    -   2.5. Data Ingestion        -   2.5.1. Input        -   2.5.2. Parsing        -   2.5.3. Indexing    -   2.6. Query Processing    -   2.7. Field Extraction    -   2.8. Data Modelling    -   2.9. Acceleration Techniques        -   2.9.1. Aggregation Technique        -   2.9.2. Keyword Index        -   2.9.3. High Performance Analytics Store        -   2.9.4. Accelerating Report Generation    -   2.10. Data Center Monitoring    -   2.11. Cloud-Based System Overview    -   2.12. Searching Externally Archived Data        -   2.12.1. ERP Process Features

3.0. Call Center Metrics

4.0. Graphical User Interface Generation

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters forms a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system 100and other embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device 102 may initiate communication with a host application 114by making a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's 102 operating state,including monitoring network traffic sent and received from the clientdevice 102 and collecting other device and/or application-specificinformation. Monitoring component 112 may be an integrated component ofa client application 110, a plug-in, an extension, or any other type ofadd-on component. Monitoring component 112 may also be a stand-aloneprocess.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem 108, such as a system 108. In such cases, the provider of thesystem 108 can implement the custom code so that performance datagenerated by the monitoring functionality is sent to the system 108 tofacilitate analysis of the performance data by a developer of the clientapplication 110 or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application 110 codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component 112 may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application 110, such assending a network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device 102 based on a requestfrom the client device 102 to download the application.

Examples of functionality that enables monitoring performance of aclient device 102 are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client device102 operations, or by making calls to an operating system and/or one ormore other applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device 102.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device 102, a manufacturer and model of the device, versionsof various software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders 204 andindexers 206 can comprise separate computer systems, or mayalternatively comprise separate processes executing on one or morecomputer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers 206. Forwarders 204 can also perform operations onthe data before forwarding, including removing extraneous data,detecting timestamps in the data, parsing data, indexing data, routingdata based on criteria relating to the data being routed, and/orperforming other data transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer 206.For example, a forwarder 204 may perform keyword extractions on raw dataor parse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally, or alternatively, a forwarder 204 mayperform routing of events to indexers 206. Data store 208 may containevents derived from machine data from a variety of sources allpertaining to the same component in an IT environment, and this data maybe produced by the machine in question or by other components in the ITenvironment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally, or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

2.9. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.9.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 6 illustrates how a search query 602received from a client at a search head 210 can split into two phases,including: (1) subtasks 604 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 606 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 604, and then distributes searchquery 604 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 606 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.9.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.9.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.9.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criterion, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.10. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems).

2.11. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders 204, indexers 206, and search heads 210. In someenvironments, a user of a data intake and query system 108 may installand configure, on computing devices owned and operated by the user, oneor more software applications that implement some or all of these systemcomponents. For example, a user may install a software application onserver computers owned by the user and configure each server to operateas one or more of a forwarder 204, an indexer 206, a search head 210,etc. This arrangement generally may be referred to as an “on-premises”solution. That is, the system 108 is installed and operates on computingdevices directly controlled by the user of the system. Some users mayprefer an on-premises solution because it may provide a greater level ofcontrol over the configuration of certain aspects of the system (e.g.,security, privacy, standards, controls, etc.). However, other users mayinstead prefer an arrangement in which the user is not directlyresponsible for providing and managing the computing devices upon whichvarious components of system 108 operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system 108 instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device 102 to interface with the remote computing resources.For example, a service provider may provide a cloud-based data intakeand query system 108 by managing computing resources configured toimplement various aspects of the system (e.g., forwarders 204, indexers206, search heads, etc.) and by providing access to the system to endusers via a network. Typically, a user may pay a subscription or otherfee to use such a service. Each subscribing user of the cloud-basedservice may be provided with an account that enables the user toconfigure a customized cloud-based system based on the user'spreferences.

FIG. 7 illustrates a block diagram of an example cloud-based data intakeand query system 706. Similar to the system of FIG. 2, the networkedcomputer system 700 includes input data sources 202 and forwarders 204.These input data sources 202 and forwarders 204 may be in a subscriber'sprivate computing environment. Alternatively, they might be directlymanaged by the service provider as part of the cloud service. In theexample system 700, one or more forwarders 204 and client devices 702are coupled to a cloud-based data intake and query system 706 via one ormore networks 704. Network 704 broadly represents one or more LANs,WANs, cellular networks, intranetworks, internetworks, etc., using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet, and is used by client devices 702 andforwarders 204 to access the system 706. Similar to the system of 108,each of the forwarders 204 may be configured to receive data from aninput source and to forward the data to other components of the system706 for further processing.

In an embodiment, a cloud-based data intake and query system 706 maycomprise a plurality of system instances 708. In general, each systeminstance 708 may include one or more computing resources managed by aprovider of the cloud-based system 706 made available to a particularsubscriber. The computing resources comprising a system instance 708may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders 204, indexers 206, searchheads 210, and other components of a data intake and query system 108,similar to system 108. As indicated above, a subscriber may use a webbrowser or other application of a client device 702 to access a webportal or other interface that enables the subscriber to configure aninstance 708.

Providing a data intake and query system 108 as described in referenceto system 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders 204, indexers206 and search heads 210) may at times refer to various configurationfiles stored locally at each component. These configuration filestypically may involve some level of user configuration to accommodateparticular types of data a user desires to analyze and to account forother user preferences. However, in a cloud-based service context, userstypically may not have direct access to the underlying computingresources implementing the various system components (e.g., thecomputing resources comprising each system instance 708) and may desireto make such configurations indirectly, for example, using one or moreweb-based interfaces. Thus, the techniques and systems described hereinfor providing user interfaces that enable a user to configure sourcetype definitions are applicable to both on-premises and cloud-basedservice contexts, or some combination thereof (e.g., a hybrid systemwhere both an on-premises environment such as SPLUNK® ENTERPRISE and acloud-based environment such as SPLUNK CLOUD™ are centrally visible).

2.12. Searching Externally Archived Data

FIG. 8 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system 108. Suchfacilities are available in the HUNK® system provided by Splunk Inc. ofSan Francisco, Calif. HUNK® represents an analytics platform thatenables business and IT teams to rapidly explore, analyze, and visualizedata in Hadoop 814 and NoSQL data stores 208.

The search head 210 of the data intake and query system 108 receivessearch requests from one or more client devices 804 over networkconnections 820. As discussed above, the data intake and query system108 may reside in an enterprise location, in the cloud, etc. FIG. 8illustrates that multiple client devices 804 a, 804 b, . . . , 804 n maycommunicate with the data intake and query system 108. The clientdevices 804 may communicate with the data intake and query system 108using a variety of connections. For example, one client device 804 inFIG. 8 is illustrated as communicating over an Internet (Web) protocol,another client device 804 is illustrated as communicating via a commandline interface, and another client device 804 is illustrated ascommunicating via a system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 804 references an index maintained by the data intake and querysystem 108, then the search head 210 connects to one or more indexers206 of the data intake and query system 108 for the index referenced inthe request parameters. That is, if the request parameters of the searchrequest reference an index, then the search head 210 accesses the datain the index via the indexer 206. The data intake and query system 108may include one or more indexers 206, depending on system accessresources and requirements. As described further below, the indexers 206retrieve data from their respective local data stores 208 as specifiedin the search request. The indexers 206 and their respective data stores208 can comprise one or more storage devices and typically reside on thesame system, though they may be connected via a local networkconnection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system 108, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 810. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process 810, 812 provides an interface through which the search head210 may access virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head 210 may access through one or more ERPprocesses 810, 812. FIG. 8 shows two ERP processes 810, 812 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 814 (e.g., Amazon S3, Amazon EMR, otherHadoop Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 816. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 810, 812indicate optional additional ERP processes 810, 812 of the data intakeand query system 108. An ERP process 810, 812 may be a computer processthat is initiated or spawned by the search head 210 and is executed bythe search data intake and query system 108. Alternatively, oradditionally, an ERP process 810, 812 may be a process spawned by thesearch head 210 on the same or different host system as the search head210 resides.

The search head 210 may spawn a single ERP process 810, 812 in responseto multiple virtual indices referenced in a search request, or thesearch head 210 may spawn different ERP processes 810, 812 for differentvirtual indices. Generally, virtual indices that share common dataconfigurations or protocols may share ERP processes 810, 812. Forexample, all search query references to a Hadoop file system 814 may beprocessed by the same ERP process 810, 812, if the ERP process 810, 812is suitably configured. Likewise, all search query references to an SQLdatabase may be processed by the same ERP process 810, 812. In addition,the search head 210 may provide a common ERP process 810, 812 for commonexternal data source 202 types (e.g., a common vendor may utilize acommon ERP process 810, 812, even if the vendor includes different datastorage system types, such as Hadoop 814 and SQL). Common indexingschemes also may be handled by common ERP processes 810, 812, such asflat text files or Weblog files.

The search head 210 determines the number of ERP processes 810, 812 tobe initiated via the use of configuration parameters that are includedin a search request message. Generally, there is a one-to-manyrelationship between an external results provider “family” and ERPprocesses 810, 812. There is also a one-to-many relationship between anERP process 810, 812 and corresponding virtual indices that are referredto in a search request. For example, using RDBMS 816, assume twoindependent instances of such a system by one vendor, such as one RDBMS816 for production and another RDBMS 816 used for development. In such asituation, it is likely preferable (but optional) to use two ERPprocesses 810, 812 to maintain the independent operation as betweenproduction and development data. Both of the ERPs, however, will belongto the same family, because the two RDBMS 816 system types are from thesame vendor.

The ERP processes 810, 812 receive a search request from the search head210. The search head 210 may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process 810, 812 may receive a search request as a result ofanalysis performed by the search head 210 or by a different systemprocess. The ERP processes 810, 812 can communicate with the search head210 via conventional input/output routines (e.g., standard in/standardout, etc.). In this way, the ERP process 810, 812 receives the searchrequest from a client device 804 such that the search request may beefficiently executed at the corresponding external virtual index.

The ERP processes 810, 812 may be implemented as a process of the dataintake and query system 108. Each ERP Process 810, 812 may be providedby the data intake and query system 108, or may be provided by processor application providers who are independent of the data intake andquery system 108. Each respective ERP Process 810, 812 may include aninterface application installed at a computer of the external resultprovider that ensures proper communication between the search supportsystem and the external result provider. The ERP processes 810, 812generate appropriate search requests in the protocol and syntax of therespective virtual indices 814, 816, each of which corresponds to thesearch request received by the search head 210. Upon receiving searchresults from their corresponding virtual indices, the respective ERPprocess 810, 812 passes the result to the search head 210, which mayreturn or display the results or a processed set of results based on thereturned results to the respective client device 804.

Client devices 804 may communicate with the data intake and query system108 through a network interface 820, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.12.1. ERP Process Features

The ERP processes 810, 812 described above may include two operationmodes: a streaming mode and a reporting mode. The ERP processes 810, 812can operate in streaming mode only, in reporting mode only, or in bothmodes simultaneously. Operating in both modes simultaneously is referredto as mixed mode operation. In a mixed mode operation, the ERP at somepoint can stop providing the search head 210 with streaming results andonly provide reporting results thereafter, or the search head 210 atsome point may start ignoring streaming results it has been using andonly use reporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head 210, whichin turn provides results to the requesting client device 804. ERPoperation with such multiple modes provides greater performanceflexibility with regard to report time, search latency, and resourceutilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source 202) are provided to the searchhead 210, which can then process the results data (e.g., break the rawdata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources 202,and/or from data stores 208 of the search head 210. The search head 210performs such processing and can immediately start returning interim(streaming mode) results to the user at the requesting client device804; simultaneously, the search head 210 is waiting for the ERP process810, 812 to process the data it is retrieving from the external datasource 202 as a result of the concurrently executing reporting mode.

In some instances, the ERP process 810, 812 initially operates in amixed mode, such that the streaming mode operates to enable the ERPquickly to return interim results (e.g., some of the raw or unprocesseddata necessary to respond to a search request) to the search head 210,enabling the search head 210 to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of raw datain a manner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head210, the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head 210 and switching to reporting mode only. The ERP at thispoint starts sending interim results in reporting mode to the searchhead 210, which in turn may then present this processed data responsiveto the search request to the client or search requester. Typically, thesearch head 210 switches from using results from the ERP's streamingmode of operation to results from the ERP's reporting mode of operationwhen the higher bandwidth results from the reporting mode outstrip theamount of data processed by the search head 210 in the streaming mode ofERP operation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head 210 forprocessing all the raw data. In addition, the ERP may optionally directanother processor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head 210 couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's 210 switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process 810, 812 (or an externalsystem) performing event breaking, time stamping, filtering of events tomatch the search query request, and calculating statistics on theresults. The user can request particular types of data, such as if thesearch query itself involves types of events, or the search request mayask for statistics on data, such as on events that meet the searchrequest. In either case, the search head 210 understands the querylanguage used in the received query request, which may be a proprietarylanguage. One exemplary query language is Splunk Processing Language(SPL) developed by the assignee of the application, Splunk Inc. Thesearch head 210 typically understands how to use that language to obtaindata from the indexers 206, which store data in a format used by theSPLUNK® Enterprise system.

The ERP processes 810, 812 support the search head 210, as the searchhead 210 is not ordinarily configured to understand the format in whichdata is stored in external data sources 202 such as Hadoop 814 or SQLdata systems. Rather, the ERP process 810, 812 performs that translationfrom the query submitted in the search support system's native format(e.g., SPL if SPLUNK® ENTERPRISE is used as the search support system)to a search query request format that will be accepted by thecorresponding external data system. The external data system typicallystores data in a different format from that of the search supportsystem's native index format, and it utilizes a different query language(e.g., SQL or MapReduce, rather than SPL or the like).

As noted, the ERP process 810, 812 can operate in the streaming modealone. After the ERP process 810, 812 has performed the translation ofthe query request and received raw results from the streaming mode, thesearch head 210 can integrate the returned data with any data obtainedfrom local data sources 202 (e.g., native to the search support system),other external data sources 202, and other ERP processes 810, 812 (ifsuch operations were required to satisfy the terms of the search query).An advantage of mixed mode operation is that, in addition to streamingmode, the ERP process 810, 812 is also executing concurrently inreporting mode. Thus, the ERP process 810, 812 (rather than the searchhead 210) is processing query results (e.g., performing event breaking,timestamping, filtering, possibly calculating statistics if required tobe responsive to the search query request, etc.). It should be apparentto those skilled in the art that additional time is needed for the ERPprocess 810, 812 to perform the processing in such a configuration.Therefore, the streaming mode will allow the search head 210 to startreturning interim results to the user at the client device 804 beforethe ERP process 810, 812 can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process 810, 812 determines that theswitchover is appropriate, such as when the ERP process 810, 812determines it can begin returning meaningful results from its reportingmode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess 810, 812 does begin returning report results, it returns moreprocessed results than in the streaming mode, because, e.g., statisticsonly need to be calculated to be responsive to the search request. Thatis, the ERP process 810, 812 doesn't have to take time to first returnraw data to the search head 210. As noted, the ERP process 810, 812could be configured to operate in streaming mode alone and return justthe raw data for the search head 210 to process in a way that isresponsive to the search request. Alternatively, the ERP process 810,812 can be configured to operate in the reporting mode only. Also, theERP process 810, 812 can be configured to operate in streaming mode andreporting mode concurrently, as described, with the ERP process 810, 812stopping the transmission of streaming results to the search head 210when the concurrently running reporting mode has caught up and startedproviding results. The reporting mode does not require the processing ofall raw data that is responsive to the search query request before theERP process 810, 812 starts returning results; rather, the reportingmode usually performs processing of chunks of events and returns theprocessing results to the search head 210 for each chunk.

For example, an ERP process 810, 812 can be configured to merely returnthe contents of a search result file verbatim, with little or noprocessing of results. That way, the search head 210 performs allprocessing (such as parsing byte streams into events, filtering, etc.).The ERP process 810, 812 can be configured to perform additionalintelligence, such as analyzing the search request and handling all thecomputation that a native search indexer 206 process would otherwiseperform. In this way, the configured ERP process 810, 812 providesgreater flexibility in features while operating according to desiredpreferences, such as response latency and resource requirements.

2.12. It Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators (KPI's).

One or more Key Performance Indicators are defined for a service withinthe SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPI measures anaspect of service performance at a point in time or over a period oftime (aspect KPI's). Each KPI is defined by a search query that derivesa KPI value from the machine data of events associated with the entitiesthat provide the service. Information in the entity definitions may beused to identify the appropriate events at the time a KPI is defined orwhenever a KPI value is being determined. The KPI values derived overtime may be stored to build a valuable repository of current andhistorical performance information for the service, and the repository,itself, may be subject to search query processing. Aggregate KPIs may bedefined to provide a measure of service performance calculated from aset of service aspect KPI values; this aggregate may even be takenacross defined timeframes and/or across multiple services. A particularservice may have an aggregate KPI derived from substantially all of theaspect KPI's of the service to indicate an overall health score for theservice.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in agraphic user interface (GUI), for example. (The service dependencytopology is like a “map” showing how services are connected based ontheir dependencies.) The service topology may itself be depicted in aGUI and may be interactive to allow navigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. Call Center Metrics

The system described above may be used to perform an analysis on a callcenter. FIG. 9 illustrates a block diagram of a system in accordancewith disclosed embodiments. As shown in FIG. 9, a call center 900 iscommunicatively coupled to a data intake and query system 108. The callcenter 900 may be part of the data intake and query system 108 or may beseparate from the data intake and query system 108. The call center 900may include call center personnel 902, call center devices 904, and acall center data store 906.

The call center personnel 902 are the individuals in the call centerthat interact with customers or manage the call center. For example, thecall center personnel may be employees, contract workers, or otherindividuals associated with the call center. The call center personnel902 have various roles, including agents 908, supervisors 910, anddirector(s) 912. Agents 908 may provide a first level of interactionwith customers. In other words, agents 908 receive initial communicationfrom customers. The metrics to evaluate agents 908 may include number ofcommunication sessions processed, length of time per communicationsession, number of additional products sold by the agents, averagestatisfaction rating of customers, other metrics, or a combinationthereof. Supervisors 910 supervise the agents 908. Supervisors 910 mayprovide a second level of contact to the agents. Metrics to evaluatesupervisors 910 may be customer rating, feedback of agents, number ofescalated problems of customers successfully processed, other metrics,or a combination thereof. The director(s) 912 manage the end to endenvironment of the call center 900. The director(s) 912 may performdecision making for the remainder of the call center 900.

The call center 900 includes call center devices 904. The call centerdevices 904 are the various electronic equipment that connect callcenter personnel 902 to customers. The call center devices 904 includesphones, computing systems, contact manager servers executing contactmanagement software, servers executing automated response and grouprouting to particular groups of agents, device routers for routingcommunications belonging to a communication session between devices,gateway device(s), and other such devices. Each call center device 904may include monitoring and reporting capability. In other words, thecall center devices 904 may include functionality to capture raw machinedata. The raw machine data may be logs of connections to and from thedevice, the routing of the device, and other information. The callcenter devices may be configured to send the raw machine data directlyor indirectly to the data intake and query system 108. The call centerdevices 904 may be a data source 202 described above with reference toFIG. 1 and the data intake and query system 108 may be configured toprocess the raw machine data as described above in response to a query.By way of another example, the call center devices 904 may be configuredto transmit the raw machine data to an intermediate data store 906, suchas data store 906. The intermediate data store 906 may be configured toprocess the raw machine data and generate processed data. The processeddata may be in the form of records in a relational database. The recordsmay be transmitted to the data intake and query system 108 describedbelow.

The call center devices 904 may be communicatively connected to a callcenter data store 906. The call center data store 906 is any storageunit, device, or collection of devices for storing call center data. Inparticular, the call center data store 906 may store personnel groupassignments 914 and personnel device assignments 916. Personnel groupassignments 914 define relationships between call center personnel 902and the groups to which the personnel are assigned. A group is assigneda set of support tasks. The, support task represent the type of problemsand questions that are routed to the group. Personnel in the same groupare redundant endpoints to the call center. Namely, any communication toa member (e.g., agent) of the group may be handled by any member of thegroup. By way of a more specific example, one group may be assigned thesupport task of managing billing questions, another group may beassigned the support task of handling connection requests, another groupmay be assigned a support task of handling service outages, etc. Thepersonnel group assignments 914 may relate an agent/supervisoridentifier to a group identifier. The agent/supervisor identifier is anyunique identifier of the agent or supervisor (e.g., employee identifier,social security number, driver's license number, or other identifier).The group identifier is any unique identifier of the group.

The personnel device assignments 916 relate the personnel to the devicesused by the personnel. For example, the personnel device assignments 916may include the media access control (MAC) address, Internet Protocol(IP) address, or other unique identifier of the call center device usedby an agent and/or supervisor as well as the unique identifier of theagent and/or supervisor.

As discussed above, the call center 900 is communicatively connected tothe data intake and query system 108. For example, the call center 900may be a part of the data intake and query system 108, directlyconnected to the data intake and query system 108, connected via anetwork to the data intake and query system 108 or otherwise connectedsuch that communications may be transmitted between the call center 900and the data intake and query system 108. The data intake and querysystem 108 may correspond to the data intake and query system 108discussed above with reference to FIG. 1.

The data intake and query system 108 may include functionality todetermine interaction metrics for a call center. In general, aninteraction metric is a measurement describing interactions between thepersonnel of a call center and the customers. For example, theinteraction metrics may be service level, total number of calls, totalnumber of calls handled within a time frame, number of agents,efficiency of agents, utilization of agents, optimal number of agents,number of contacts made by agents, number of calls escalated to asupervisor, average handle time, number of calls closing an issue of acustomer or resulting in a sale to a customer, or other metric. By wayof another set of examples, the interaction metric may be a statistic(e.g., average, variance, median, etc.) about the service level, totalnumber of calls, total number of calls handled within a time frame,number of agents, efficiency of agents, utilization of agents, optimalnumber of agents, number of contacts made by agents, number of callsescalated to a supervisor, number of calls closing an issue of acustomer or resulting in a sale to a customer, or other metric. Theservice level is the percentage of calls answered within a predefinedamount of time. The efficiency of the agents may be based on the speedat which agents process the calls to completion. The utilization of theagents is the percentage of time call center agents are on calls or inafter-call work (e.g., reporting on the call). The optimal number ofagents is the number of agents that are estimated to achieve at leastone predefined threshold of another metric or metrics. In one or moreembodiments, the optimal number is defined such that the predefinedthreshold is just achieved and additional agents cause the metric toexceed the threshold more than the optimal number, do not cause anychange in the metric or cause the metric to be below the threshold.

In one or more embodiments, interaction metrics are defined on a callcenter basis and a per group basis. For example, an interaction metricmay be generated for an entire call center. By way of another example,an interaction metric may be generated individually for one or moregroups of the call center. For example, each group of the call centermay have an individual interaction metric that is defined for the group.The individual interaction metric may be independent on other groups ordependent on other groups. For example, the optimal number of agents mayassume that unlimited number of agents are assigned to the call center.In the example, the determining of the optimal number of agents for agroup is independent on the optimal number of agents assigned to othergroups. By way of another example, the optimal number of agents mayaccount for the total number of agents available to the call center. Insuch a scenario, the optimal number of agents for a group is dependenton the number of agents for other groups and total number of agentsavailable.

In one or more embodiments, interaction metrics may be past interactionmetrics, current interaction metrics and future interaction metrics.Past interaction metrics are interaction metrics defined for a timeperiod before the current time period. Current interaction metrics aredefined for the current time period. Future interaction metrics areinteraction metrics defined for after the current time period. In someembodiments, the current time period is based on real-time reporting andmonitoring of interaction metrics. For example, the current time periodmay be the current hour, the current ten-minute interval, the currentset of three hours, etc. Current interaction metrics may be based oncurrent interaction data. Current interaction data is data collectedfrom the call center devices 904 within the current time period.

The data intake and query system 108 implementing call center analysismay include an interface 920, a call center query generator 922, asearch head 210, one or more indexers 206, one or more data stores 208,and one or more forwarders 204. The search head 210, one or moreindexers 206, one or more data stores 208, and one or more forwarders204 may be the same or similar to the corresponding components of FIG.2. In particular, the forwarders 204 may include functionality toreceive processed data from the call center 900. The forwarders 204 mayinclude functionality to forward the processed data to the data stores208 via the indexers 206. Further, the forwarders 204 may includefunctionality to receive raw machine data directly or indirectly fromthe call center devices 906. The forwarders 204 may includefunctionality to forward the raw machine data to the data stores 208 viathe indexers 206. The data stores 208 may store the raw machine data asraw machine data for processing when a request for an interaction metricis received.

The search head 210 in FIG. 9 may be the same or similar to the searchhead 210 discussed above with reference to FIG. 2. The search head 210in FIG. 9 implementing the call center analysis may include thefunctionality and the components of the search head discussed below withreference to FIG. 10. The search head 210 includes functionality toexecute a query and generate an interaction metric.

The interface 920 is the instructions for an application programminginterface or graphical user interface that includes functionality todirectly or indirectly interact with a user. For example, the interface920 may be an interface of the reporting application described above. Byway of another example, the interface 920 may be a report generationinterface. The interface 920 may execute on a computing device, such asa server implementing a front end of a web application. The interfaceincludes functionality to receive a request for one or more interactionmetrics, trigger generating the one or more interaction metrics, andproviding, directly or indirectly, the one or more interaction metricsto a user.

In one or more embodiments, the interface includes a metric interfacehierarchy 924 and a general layout 926. The metric interface hierarchy924 is hierarchy having nodes ordered in parent child relationships.Each node includes a visualization for the node. The visualizationdefines a presentation for the node. For example, the visualization mayinclude a definition of colors, a definition of a graph, a definition ofcharts, the current interaction metric presented by the node, or othervisualization. Each node may further include a unique identifier for thenode. Child nodes are dependent on the corresponding parent node. A rootnode is at a top level and independent of other nodes in the hierarchy,while a leaf node is dependent on multiple nodes of the metric interfacehierarchy. The ordering in the metric interface hierarchy 924 may bebased on an ordering of interaction metrics represented by each node. Inother words, the more general interaction metrics may correspond toparent nodes of the metric interface hierarchy 924 while the morespecific interaction metrics are child nodes of the metric interfacehierarchy 924. By way of an example, an interaction metric that is asingle value defined for the entire call center 900 may be a parent nodeof child node for multiple interaction metrics of the same type definedfor the call center over time and a parent node of child node havingmultiple interaction metrics an interaction metrics for multiple groups.The ordering of the nodes may be defined in the metric interfacehierarchy 924 separately from the visualization of the nodes. Forexample, the ordering may be defined using the unique identifier of thenodes. In one or more embodiments, the metric interface hierarchy 924 isdefined using JAVASCRIPT® Object Notation (JSON).

The general layout 926 is a template specifying locations ofvisualizations on a graphical user interface. Locations in the generallayout 926 are ordered according to the parent child relationships. Inone or more embodiments, the general layout 926 is partitioned intotiers. The tiers that are disposed above other tiers in the generallayout 926 are for parent nodes, while the tiers that are disposed belowanother tier is for child nodes. An example of a general layout 926 ispresented below in reference to FIG. 18. The graphical user interface isdiscussed in further detail in Section 4.0.

Continuing with FIG. 9, the call center query generator 922 is hardware,software, firmware or a combination thereof that includes functionalityto generate a query to obtain an interaction metric. In one or moreembodiments, the call center query generator 922 may be triggered by theinterface 920 to generate one or more queries. The query may have anordered set of pipeline commands, whereby each pipeline command mayspecify data to obtain or generate that is used for the next pipelinecommand. Each pipeline command may correspond to a phase of the query(i.e., query phase) discussed above with reference to FIG. 6. The resultof executing the query may be the interaction metric. For example, thecall center query generator 922 may be configured to generate,separately for each group, a query to determine a current interactionmetric for the group. By way of another example, the call center querygenerator 922 may be configured to determine an optimal number of agents908 based on the submission of multiple queries to the search head 210.The call center query generator 922 may be configured to aggregateresults across multiple queries.

As discussed above, queries are submitted to a search head 210. FIG. 10illustrates a block diagram of a search head 210 in accordance withdisclosed embodiments. As shown in FIG. 10, the search head 210 includesa query intake 1910 and query phase processors 1004, 1006. A query phaseprocessor 1004, 1006 may be hardware, software, firmware, or anycombination thereof. The query phase processor 1004, 1006 includesinstructions for executing a query phase when provided with theparameters of the query phase. In one or more embodiments, each queryphase processor 1004 is specific to the type of query phase. In otherwords, the query phase processor 1004, 1006 has a one to onecorrespondence with possible query phases that may be in the query. Thequery phase processor 1004, 1006 is configured to receive a request toexecute a pipeline command, where the request includes parameters, andexecute the instructions of the query phase processor 1004, 1006 usingthe parameters. The query phase processor 1004 further includesfunctionality to return results from executing a query phase. The queryphase processor 1004, 1006 may be predefined by the system anddistributed with the search head 210, created by an informationtechnology specialist, created by a software developer after the searchhead 210 is distributed (e.g., as a plug in to the search head 210), orby another. Further, although only three query phase processors 1004,1006 are shown, more query phase processors 1004, 1006 may exist withoutdeparting from the scope of the invention. By way of a more specificexample, a query phase processor 1004 may include a search query phaseprocessor that executes a search pipeline command to search the datastores 208 and obtain events. In one or more embodiments, the searchphase processor may include functionality to invoke the indexers 206 andmanage the search to obtain results. The results may be a portion of rawmachine data that is associated with a timestamp. A query phaseprocessor 1004 may be an aggregation phase processor. The aggregationphase processor 1004 may include functionality to execute an evaluationpipeline command. The evaluation pipeline command is a request toaggregate data to obtain processed data. For example, if the raw machinedata includes connection and disconnection requests to a call centerdevice 904 of a call center agent 908, the aggregation may be theduration of time in which the call center agent 908 is connected. Aquery phase processor 1004 may be a statistics phase processor. Thestatistics phase processor includes functionality to execute astatistics pipeline command. A statistics pipeline command is a requestto obtain a statistic about data.

In one or more embodiments, at least one of the query phase processorsmay be a call center query phase processor 1006. The call center queryphase processor 1006 includes functionality to generate an interactionmetric for a call center 900. In one or more embodiments, the callcenter query phase processor 1006 implements ErlangC formula. ErlangCformula calculates a probability that a new customer will be added to await queue as opposed to immediately connected to an agent. The callcenter query phase processor 1006 may implement the original version ofthe ErlangC formula or may implement a modified version of the ErlangCformula. The modified version may be to replace the factorialcalculations in the ErlangC formula with Ramanujan factorialapproximation. Further, the modified version of ErlangC may change theprobability of a customer waiting to a probability of a customer notwaiting. The probability of a customer not waiting may be the sum of aexponential function approximation and an error portion. The errorportion may be a summation. Further optimization may be performed bytruncating the summation of the error portion.

The above is an example of a call center query phase processor 1006.Other example call center query phase processors 1006 may exist that areconfigured to generate other interaction metrics. Further, although onlythree query phase processors 1004 are shown, other query phaseprocessors 1004 may exist without departing from the scope of theinvention.

FIG. 11 is a flow diagram illustrating how an interaction metric may bedetermined in accordance with disclosed embodiments. In Block 1102, adata store is queried for current interaction data between call centerpersonnel and customers. In particular, a query is sent to the datastore for current interaction data. The query may be sent according to astructured data base query (e.g., using structure query language (SQL))in the case of the interaction data being processed data. By way ofanother example, the query may be in accordance with a raw machine datasearch command. The data returned form the query includes currentinteraction data, such as real time data obtained during a current timeperiod. The current interaction data may be partitioned into groups.Each group may have corresponding current interaction data. Thepartitioning may be performed by sending separate queries to the datastore. By way of another example, the partitioning may be performed bythe remainder of the data intake and query system and/or the data store.

Different techniques may be used to determine which current interactiondata relates to which group. In at least some embodiments, currentinteraction data, such as each event in raw machine data, directlyinclude an agent identifier. By relating the agent identifier to thegroup to which the agent is assigned, the group corresponding to the rawmachine data may be identified. In some embodiments, the currentinteraction data includes a group identifier.

If the current interaction data only includes information about thephysical or virtual device to which a communication is directed,partitioning the current interaction data into groups may be based on acombining information about personnel groups assignments and personneldevice assignments to identify group device assignments. In otherembodiments, the data stores may directly store group deviceassignments. The group device assignments identify a set of call centerdevices that are assigned to each group. Some call center devices areused for multiple groups, such as to route communications to variousgroups. Thus, the current interaction data for a group may be obtainedby querying the data store for current interaction data from the set ofcall center devices assigned to a group and current interaction datadescribing communications to and from the set of call center devicesassigned to a group. By obtaining current interaction data on a pergroup basis or by having the current interaction data identify thegroups, resulting current interaction metrics may be obtained on a pergroup basis. Rather than the current interaction data being partitionedon a per group basis when obtained from the data store, the currentinteraction data may be partitioned by devices. Subsequently, thecurrent interaction data may be partitioned into groups based on thegroup device assignments.

In Block 1104, for each of at least some of the call center groups, acurrent interaction metric that is specific to the call center group isdetermined. The current interaction metric is determined from thecurrent interaction data. For example, an ErlangC function may beapplied to determine the current interaction metric. The ErlangCfunction identifies the probability of a customer waiting in a queue asopposed to being immediately connected to call center personnel. Basedon the probability, various interation metrics may be determined, suchas the service level per group. By obtaining interaction metrics on aper group basis, one or more embodiments are able to identify how eachgroup compares across the call center. Directors and others may use theinformation to make staffing decisions across the call center. Forexample, rather than adding agents to equally or based on guesses tovarious groups, the director and supervisors may add agents based on theneeds of each group. Further, the director and supervisors may moveagents on an as needed basis. Additionally, by using current interactiondata, immediate changes to the personnel assignments may be performed.

Continuing with FIG. 11, in Block 1106, the current interaction metricis provided for each of the at least some call center groups. Providingthe current interaction metric may include generating a graphical userinterface as described below with the current interaction metric. Thegraphical user interface may be transmitted as instructions to theuser's computing device. Providing the current interaction metric mayinclude displaying the current interaction metric. Providing the currentinteraction metric may include transmitting the current interactionmetric via an application programming interface. By way of anotherexample, providing the current interaction metric may be to transmit analert, such as a message, to the user when the current interactionmetric fails to satisfy a criteria.

FIG. 12 is a flow diagram illustrating how to determine a currentinteraction metric on a per group basis in accordance with disclosedembodiments. In Block 1202, a request for current interaction metric foreach of multiple groups is received. For example, a request may bereceived to display a graphical user interface. For example, the requestmay be initiation of a website, a selection of a link in a website. Thecurrent interaction metric that is displayed may be a defaultinteraction metric that is displayed upon initiation of the graphicaluser interface. As another example, the user may select a link to obtaina current interaction metric. The request may specify one, more thanone, or all groups. For example, the request may include groupidentifiers.

In Block 1204, a group is selected. In one or more embodiments, thesystem processes each group in the request to obtain a currentinteraction metric for the group. The processing may be processing oneor more groups concurrently and/or in serial.

In Block 1206, for the selected group, a query having multiple pipelinecommands is generated. In one or more embodiments, the query includesmultiple pipeline commands. The pipeline commands may include a dataacquisition command to search the data store for current interactiondata, a stats command to aggregate the current interaction data, anevaluation command to evaluate the statistics and generate evaluationresults, and a call center command to generate the current interactionmetric. Each command may have parameters. The parameters of the commandmay be predefined in a predefined query. The call center query generatormay be configured to add the group identifier to the predefined query.

In Block 1208, execution of the pipeline is initiated to obtain acurrent interaction metric for the group. In one or more embodiments,the call center query generator sends the query to the search head. Thesearch head receives and processes the query using the indexers and thecommand processors. After processing the query, the search head returnsthe results.

In Block 1210, a determination is made whether to repeat for anothergroup. If a determination is made to repeat processing for anothergroup, the flow proceeds to Block 1204 to select another group. Asdiscussed above, each selected group is processed to obtain currentinteraction metrics for each selected group.

Before or after each of the multiple groups have current interactionmetrics, Block 1212 may be performed. In Block 1212, the currentinteraction metric is provided for each of the at least some call centergroups. Providing the current interaction metric for each of the atleast some call center groups may be performed as discussed above withreference to Block 1106 of FIG. 11.

FIG. 13 is a flow diagram for processing a pipeline command to determinea current interaction metric in accordance with disclosed embodiments.In other words, FIG. 13 shows a flow diagram for processing by a searchhead in accordance with one or more embodiments.

In Block 1302, a query is received. The search head receives the queryfrom the call center query generator.

In Block 1304, the query is partitioned into pipeline commands. In oneor more embodiments, the search head partitions the query into pipelinecommands by parsing the query into tokens. Each token corresponds to acommand identifier, a parameter, or a delimiter. Predefined tokens, suchas “|”, may be used to denote the end of a previous pipeline command andthe start of a new command.

In Block 1306, the first pipeline command is transmitted to a firstquery phase processor to obtain statistics using an indexer. The searchhead processes the command using the instructions of the first queryphase processor. The first query phase processor uses the parameters ofthe command to process the command. Processing of the first command toperform data acquisition may be performed as discussed above to obtainraw machine data.

In Block 1308, the second pipeline command is transmitted to a secondquery phase processor to evaluate the statistics and obtain evaluationresults. In particular, the data obtained from the first query phaseprocessor is used as input to the second query phase processor with theparameters of the second pipeline command. The second query phaseprocessor includes mathematical functions to perform statistics andobtain evaluation results.

In Block 1310, a third pipeline command is transmitted to a third queryphase processor to determine the current interaction metric. Inparticular, the data obtained from the second query phase processor isused as input to the third query phase processor with the parameters ofthe third pipeline command. The third query phase processor includesinstructions to obtain the particular current interaction metric. Thesearch head then returns the current interaction metric to the callcenter query generator. The call center query generator may provide thecurrent interaction metric to the user as discussed above.

In some embodiments, the call center query generator uses the techniquedescribed in FIGS. 12 and 13 to determine a current interaction metricdefining the optimal or a target number of agents. In other words, byrepetitively sending all or part of a query with different numbers ofagents, the call center query generator may determine the target numberof agents. In at least some embodiments, the selection of the number ofagents that is optimal is performed to reduce execution time on acomputing device. FIG. 14 is a flow diagram illustrating how todetermine a number of agents in accordance with disclosed embodiments.FIG. 14 may be performed on a per group basis.

In Block 1402, a first number of agents is selected. In one or moreembodiments, the first number of agents is the current number of agents.

In Block 1404, an estimated metric matching the selected number ofagents is obtained. The estimated metric is the interaction metric thatis estimated based on the number of agents. Initially, using the firstnumber of agents, the call center query generator generates a query asdescribed above with reference to FIG. 12. The search head processes thequery as described above with reference to FIG. 13 to obtain aninteraction metric.

In Block 1406, a determination is made whether the estimated metricsatisfies a threshold. In other words, the estimated metric is comparedagainst the threshold. If the threshold is a lower bound threshold, thenthe estimated metric satisfies the threshold when the estimated metricis greater than or equal to the threshold. If the threshold is an upperbound threshold, then the estimated metric satisfies the threshold whenthe estimated metric is less than or equal to the threshold. By way ofan example, if the threshold is 85% service level, and the currentinteraction metric is 70% service level, then the current interactionmetric does not satisfy the threshold. In one or more embodiments, thethreshold is predefined. For example, the threshold may be defined by auser of the call center, director of the call center, etc.

In Block 1408, if the estimated metric does not satisfy the threshold,then a percentage of the difference between the estimated metric and thethreshold is determined. In one or more embodiments, the differencebetween the current interaction metric and the threshold is calculated.The percentage difference is the difference divided by the thresholdmultiplied by one hundred.

In Block 1410, the number of agents is incremented based on thepercentage of the difference. The percentage difference determined inBlock 1408 is multiplied by the number of agents to obtain anintermediate result. The intermediate result is added to the number ofagents to obtain a new number of agents. By repetitively incrementingthe number based on the percentage difference, the subsequentinteraction metric is much closer to the threshold and less processingis performed. In other words, the optimal solution is achieved faster.In one or more embodiments, when the current interaction metric iswithin a predefined range of the threshold, rather than using thepercentage difference, the number of agents may be incremented by apredefined amount (e.g., 1 or 2).

After obtaining a second number of agents, the process may repeat toobtain a current interaction metric for the second number of agents.With each subsequent iteration of the process of FIG. 14, the callcenter query generator may generate a new query. In one or moreembodiments, subsequent queries use the current interaction data fromthe first query with the subsequent number of agents replacing the priornumber of agents in the pipeline commands. In other words, the dataacquisition pipeline command may be omitted in subsequent queries sothat current interaction data is not reacquired from the data store.

In Block 1412, when the estimated metric for a number of agentssatisfies the threshold in Block 1406, the number of agents is selectedas the target number of agents. In one or more embodiments, the targetnumber of agents is provided as the optimal number. Providing the targetnumber of agents may be performed as discussed above with reference toFIG. 11.

The techniques described in FIGS. 11, 12, and 13 may be used to generatea future interaction metric by correlating current interaction data withthird party data. FIG. 15 is a flow diagram illustrating how tocorrelate third party data with call center information to determine afuture interaction metric in accordance with disclosed embodiments.

In Block 1502, third party data describing an environment affecting atleast some customers is obtained. The environment may be weather in theregion of the customers (e.g., storms, sunny, cloudy, etc.), promotionalenvironment (e.g., sales promotions being offered by the business forwhich the call center is created, sales promotions by competitors),information technology environment (e.g., switching servers, data breachevent affecting at least some customers, change in software applicationsor user interfaces) or other environment. Obtaining the third party datamay be performed by sending a request to the third party using theapplication programming interface of the third party. By way of anotherexample, obtaining third party data may be performed by data scaping awebsite of a third party.

In Block 1504, an effect on the call center is predicted based on theenvironment. The effect may be the predicted volume of calls to the callcenter, the amount of time to process each call, or other effect.Predicting the effect may be performed using machine learning. In otherwords, a set of training data based on historical events may beobtained. The historical events may include the environment and the callcenter interaction data at the time of the existence of the environment.Machine learning may be employed to determine which call centerinteraction data is affected by the environment and how the call centerinteraction data is effected by the environment. For example, stormyweather may result in more calls to a call center processing pizzaorders. By way of another example, a change in user interfaces by atechnology company may result in longer calls to the technology companyso that users are able to learn the new user interface. The effect maybe on a group basis. For example, a change in user interface may onlyaffect a group that manages information technology problems. By way ofanother example, a change in promotional environment may affects salesfor a particular product, information technology, and billing, but not agroup that manages sales for other products. The degree of the effect(e.g., amount of increase or decrease in call volume, amount of theincrease or decrease in length of time to handle a call) may be learnedusing machine learning.

In Block 1506, a future interaction metric is determined for the callcenter using the current interaction data and the effect on the callcenter. From the effect on the call center, the current interaction datais updated to match the effect. For example, if an increase in 100 callsis expected based on the environment, then the current interaction dataobtained from the data store is updated to increase the call volume by100. For example, the call center query generator may include a pipelinecommand to obtain current interaction data for the call center or agroup thereof, and modify the current interaction data to match theeffect. The call center query generator may further include pipelinecommands as discussed above in FIGS. 12 and 13 to obtain futureinteraction metrics based on the modified current interaction data. Thefuture interaction metric may be provided in a same or similar mannerdiscussed above with reference to FIG. 11.

One or more embodiments may also use machine learning to predict afuture volume of calls. FIG. 16 is a flow diagram illustrating how topredict a future volume of calls in accordance with disclosedembodiments. In Block 1602, an expected volume of calls is determinedfor a time period. The expected volume of calls may be determined basedon the environment, historical trends based on the day of the year, orother information. The time period may be an expanded time period of thecurrent time period. For example, the current time period may be thecurrent four hours, and the expanded time period may be the day. By wayof another example, the expanded time period may be the length of timethat a promotion is operating.

In Block 1604, a current interaction metric is obtained. In one or moreembodiments, the current interaction metric is obtained for the currenttime period. More particularly, the current interaction metric describesa performance of a call center within the time period to a current pointin time.

In Block 1606, a future volume of calls to the call center 900 ispredicted for the remainder of the time period. Machine learning may beapplied based on the expected volume of calls and the currentinteraction metric to obtain a future volume of calls. The future volumeof calls may be for the remainder of the time period (e.g., theremainder of the day).

In Block 1606, the future interaction metric is determined using thefuture volume of calls. From the future volume of calls and the currentinteraction metric the call center query generator may generate a queryto obtain a future interaction metric. The future interaction metric maybe provided as discussed above. Using the future interaction metric, theuser may determine how to adjust personnel in the call center.

4.0. Graphical User Interface Generation

The various metrics may be presented in a graphical user interface usinga metric interface hierarchy and a general interface. FIG. 17 is a flowdiagram illustrating how to generate a graphical user interface inaccordance with disclosed embodiments.

In Block 1702, a metric interface hierarchy having nodes is obtained,where each node defines a visualization for the node. For example, themetric interface hierarchy may be obtained from a data store.

In Block 1704, a selection of a node is received. In one or moreembodiments, when the graphical user interface is initially displayed adefault interaction metric is displayed. In such a scenario, the nodecorresponding to the default interaction metric is selected in Block1704. By way of another example, a user may select a button or link in acurrently displayed graphical user interface to select an interactionmetric. In such a scenario, the node corresponding to the interactionmetric is selected in Block 1704. In one or more embodiments,attributes, such as relationships between the nodes and the interactionmetrics and default interaction metric, are defined in the metricinterface hierarchy. Thus, identifying the node corresponding to theinteraction metric may be determine directly from the metric interfacehierarchy in accordance with one or more embodiments.

In Block 1706, the metric interface hierarchy is traversed starting withthe selected node to obtain a subhierarchy. In other words, the nodesthat are direct and indirect child nodes of the selected node areobtained from the metric interface hierarchy. The result is asubhierarchy having a root node of the selected node.

In Block 1708, a graphical user interface is created from a generalinterface by populating the general interface with the visualizationfrom each node of the subhierarchy according to the ordering. Thegeneral interface includes placeholders for the visualizationcorresponding to the root node, the visualization corresponding to eachchild node, etc., to the visualizations corresponding to the leaf nodesof the hierarchy. The placeholders are populated according to theordering on the nodes in the subhierarchy with the visualizationsspecified by the nodes. In particular, any graphs, charts, graphics orother visualizations are generated as specified in the nodes. Thegenerated visualizations are placed into the graphical user interface inthe position specified by the general interface based on the position orordering of the nodes within the subhierarchy.

In Block 1710, the graphical user interface is provided in accordancewith one or more embodiments of the invention. Providing the graphicaluser interface may be performed as discussed above with reference toFIG. 11.

FIG. 18 is a diagram of a general interface 1800 in accordance withdisclosed embodiments. As shown in the diagram, the general interface1800 has a title position 1802, which is the location of the title. Thegeneral interface 1800 is further has multiple tiers 1804, 1806, 1808,1810. The first tier 1804 has positions for selector buttons to select afilter. The second tier 1806 has a position for the root node of thesubhierarchy and to select from multiple possible interaction metrics.In particular, each interaction metric that may be selected as a rootnode of the subhierarchy may be in the second tier 1806. The selectedinteraction metric is the root node of the subhierarchy. Thevisualization defined in the root node is presented in the second tier.

The third tier 1808 has the position(s) for the visualization(s) ofdirect child or children of the root node. As shown, the third tier 1808is disposed below the second tier 1806. Although FIG. 18 shows a singleposition, the general interface 1800 may include instructions fordynamically setting the number of positions according to the number ofchild nodes in the tier. If multiple children nodes exist, then thevisualization of the additional children nodes may be placed adjacent toeach other, with or without automated resizing to accommodate thevisualizations from the multiple nodes.

The fourth tier 1810 has the position(s) for the visualization of theleaf node(s) of the subhierarchy. As with the third tier 1808, althoughFIG. 18 shows three positions, the general interface 1800 may includeinstructions for dynamically setting the number of positions accordingto the number of leaf nodes (e.g., by placing the additional nodesadjacent to each other or automated resizing of the positions). Thefourth tier 1810 is disposed underneath the third tier 1808. If multipleadditional levels of the hierarchy exist (e.g., direct children of thedirect children of the root node that are not leaf nodes), then theadditional levels may be placed between the third tier 1808 and thefourth tier 1810. In other words, the fourth tier may not be disposeddirectly under the third tier 1808, but rather indirectly disposed underthe third tier 1808. More specifically, the general interface 1800 mayinclude instructions for dynamically adjusting the number of tiersaccording to the number of levels of the subhierarchy.

FIG. 19 is an example graphical user interface 1900 generated using thegeneral interface in accordance with the disclosed embodiments. In FIG.19, the graphical user interface 1900 includes the title 1902 of callcenter status. The select button for service level 1904 is selected.Because service level 1904 is selected, the subhierarchy corresponds tointeraction metrics related to service level. As shown, the otherpossible interaction metrics to select includes calls offered, handled,active agents, and agent efficiency. Each interaction metric may have acorresponding value presented in the graphical user interface 1900 thatis defined for the entire call center. The second tier corresponds to anode of the service level subhierarchy including parameters of a graphfor service level over time. When rendered after performing theoperations described above to obtain the interaction metrics, the graph1906 is displayed. The third tier corresponds to multiple nodes that arechild nodes of the service level over time. For example, one nodeincludes in the visualization parameters of a chart for service levelper group. When rendered after performing the operations described abovewith reference to FIG. 11, chart 1908 is displayed. Another nodeincludes parameters of a chart for current staffing to target staffingper group. When rendered after performing the operations described abovewith reference to FIG. 13, chart 1910 is displayed. Another nodeincludes parameters of a graph for service level over time per group.When rendered after performing the operations described above withreference to FIG. 11, graph 1912 is displayed.

FIG. 20 is an example graphical user interface 2000 generated using thegeneral interface in accordance with the disclosed embodiments. In FIG.20, the graphical user interface 2000 includes the title 2002 of callcenter status. The select button for calls offered 2004 is selected.Because calls offered 2004 is selected, the subhierarchy corresponds tointeraction metrics related to calls offered. The second tiercorresponds to a node of the calls offered subhierarchy includingparameters of a graph for call volume. When rendered after performingthe operations described above to obtain the interaction metrics, thegraph 2006 is displayed. The third tier corresponds to multiple nodesthat are child nodes of the call volume over time. For example, one nodeincludes in the visualization parameters of a chart for increase in callvolume per group. When rendered after performing the operationsdescribed above with reference to FIG. 11, chart 2008 is displayed.Another node includes parameters of a target staffing for call volumeper group. When rendered after performing the operations described abovewith reference to FIG. 13, chart 2010 is displayed. Another nodeincludes parameters of a graph for number of abandoned calls andprocessed calls. When rendered after performing the operations describedabove with reference to FIG. 11, graph 2012 is displayed.

FIG. 21 is an example graphical user interface 2100 generated usinganother general interface in accordance with the disclosed embodiments.In FIG. 21, the graphical user interface 2100 includes the title 2102 ofPeer Groups-Agent Groups. In other words, information about peer groupsis selected as the first node. Various visualizations are provided inthe first tier 2106 that span the call center. The second tier includesa chart 2108 of calls per time period per group. After performing theoperations discussed above with reference to FIG. 11, chart 2108 isdisplayed.

FIG. 22 is an example graphical user interface 2200 presenting staffingperformance in accordance with the disclosed embodiments. The top row2202 presents visualizations of the expected volume and staffing for thecurrent day. The second row 2204 presents current interaction metricsincluding the current service level, the current staffing versus plannedstaffing, and a comparison of the call volume to forecasted volume. Thethird row 2206 presents projected interaction metrics for the remainderof the time period (i.e., the current day in the example). The third row2206 may be generated using machine learning to project call volume forthe rest of the day based on current volume. The resulting projectionmay be used to calculate target staffing levels for the remainder of theday, which is presented in the graphical user interface.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A method of generating a graphical userinterface, the method comprising: obtaining a metric interface hierarchycomprising a plurality of nodes, each node of the plurality of nodesrepresenting an interaction metric and defining a visualization for thenode, the metric interface hierarchy defining an ordering on theplurality of nodes; traversing the metric interface hierarchy startingwith a selected node to obtain a subhierarchy; creating the graphicaluser interface from a general interface by, for each node of thesubhierarchy: obtaining an interaction metric for the node, creating thevisualization of the interaction metric as defined for the node, andpopulating the general interface with the visualization of theinteraction metric, wherein the general interface defines a dynamicallyadjustable layout of panes, and wherein populating the general interfacecomprises adding the visualization of the interaction metric to a panematching a location of the node in the ordering defined by the metricinterface hierarchy; and providing the graphical user interface.
 2. Themethod of claim 1, wherein the general interface comprises: a pluralityof filter buttons, the plurality of filter buttons for applying a filterfunction to a calculation of metrics defined by at least one node of thesubhierarchy.
 3. The method of claim 1, wherein the general interfacecomprises: a second tier for displaying the visualization of a root nodeof the subhierarchy, the root node being in the plurality of nodes; athird tier for displaying the visualization of each direct child node ofthe root node, the each direct child node being in the plurality ofnodes, the third tier disposed under the second tier; and a fourth tierfor displaying the visualization of each leaf node of the subhierarchy,the each leaf node being in the plurality of nodes, the fourth tierdisposed under the third tier.
 4. The method of claim 1, wherein thesubhierarchy comprises: a first node comprising parameters of a graphfor service level over time, a second node comprising parameters of achart for service level per group, a third node comprising parameters ofa chart for current staffing to target staffing per group, and a fourthnode comprising parameters of a graph for service level over time pergroup, wherein the second node, the third node, and the fourth node arechildren nodes of the first node in the subhierarchy.
 5. The method ofclaim 1, wherein the metric interface hierarchy comprises: a first nodecomprising parameters of a graph for call volume, a second nodecomprising parameters of a chart for increase in call volume per group,a third node comprising parameters of a chart for target staffing forcall volume per group, and a fourth node comprising parameters of agraph for number of abandoned calls and processed calls, wherein thesecond node, the third node, and the fourth node are children nodes ofthe first node in the metric interface hierarchy.
 6. The method of claim1, wherein the metric interface hierarchy comprises: a first nodecomprising a visualization of calls offered per time period, and asecond node comprising parameters of a chart for calls per time periodper group.
 7. The method of claim 1, further comprising: querying a datastore for current interaction data between a plurality of call centerpersonnel and a plurality of customers, the plurality of call centerpersonnel grouped into a plurality of call center groups; determining,for at least some call center groups of the plurality of call centergroups, a current interaction metric specific to the call center group;and populating the visualization using the current interaction metricfor each of the at least some call center groups.
 8. The method of claim1, further comprising: querying a data store comprising raw machine datafor a call center, the raw machine data defining a plurality ofinteractions between a plurality of call center personnel and aplurality of customers; determining a current interaction metricspecific; and populating the visualization using the current interactionmetric.
 9. The method of claim 1, further comprising: obtaining a thirdparty data describing an environment affecting at least some of aplurality of customers; predicting an effect on the call center based onthe environment, determining a future interaction metric for a callcenter using current interaction data and the effect on the call center,and populating the visualization using the future interaction metric.10. The method of claim 1, further comprising: determining an expectedvolume of calls for a time period, obtaining current interaction datadescribing a performance of a call center within the time period to acurrent point in time; predicting, based at least in part on the currentinteraction data and the expected volume of calls, a future volume ofcalls to the call center for a remainder of the time period, theremainder of the time period being after the current point in time;determining a current interaction metric using the future volume ofcalls; and displaying the current interaction metric and the expectedvolume of calls in the graphical user interface.
 11. A systemcomprising: memory comprising instructions; and a computer processor forexecuting the instructions that cause the computer processor to performoperations comprising: obtaining a metric interface hierarchy comprisinga plurality of nodes, each node of the plurality of nodes representingan interaction metric and defining a visualization for the node, themetric interface hierarchy defining an ordering on the plurality ofnodes, traversing the metric interface hierarchy starting with aselected node to obtain a subhierarchy, creating a graphical userinterface from a general interface by, for each node of thesubhierarchy: obtaining an interaction metric for the node, creating thevisualization of the interaction metric as defined for the node,populating the general interface with the visualization of theinteraction metric, wherein the general interface defines a dynamicallyadjustable layout of panes, and wherein populating the general interfacecomprises adding the visualization of the interaction metric to a panematching a location of the node in the ordering defined by the metricinterface hierarchy, and providing the graphical user interface.
 12. Thesystem of claim 11, wherein the general interface comprises: a pluralityof filter buttons, the plurality of filter buttons for applying a filterfunction to a calculation of metrics defined by at least one node of thesubhierarchy.
 13. The system of claim 11, wherein the general interfacecomprises: a second tier for displaying the visualization of a root nodeof the subhierarchy, the root node being in the plurality of nodes; athird tier for displaying the visualization of each direct child node ofthe root node, the each direct child node being in the plurality ofnodes, the third tier disposed under the second tier; and a fourth tierfor displaying the visualization of each leaf node of the subhierarchy,the each leaf node being in the plurality of nodes, the fourth tierdisposed under the third tier.
 14. The system of claim 11, wherein thesubhierarchy comprises: a first node comprising parameters of a graphfor service level over time, a second node comprising parameters of achart for service level per group, a third node comprising parameters ofa chart for current staffing to target staffing per group, and a fourthnode comprising parameters of a graph for service level over time pergroup, wherein the second node, the third node, and the fourth node arechildren nodes of the first node in the subhierarchy.
 15. The system ofclaim 11, wherein the metric interface hierarchy comprises: a first nodecomprising parameters of a graph for call volume, a second nodecomprising parameters of a chart for increase in call volume per group,a third node comprising parameters of a chart for target staffing forcall volume per group, and a fourth node comprising parameters of agraph for number of abandoned calls and processed calls, wherein thesecond node, the third node, and the fourth node are children nodes ofthe first node in the metric interface hierarchy.
 16. The system ofclaim 11, wherein the metric interface hierarchy comprises: a first nodecomprising a visualization of calls offered per time period, and asecond node comprising parameters of a chart for calls per time periodper group.
 17. The system of claim 11, wherein the operations furthercomprise: querying a data store for current interaction data between aplurality of call center personnel and a plurality of customers, theplurality of call center personnel grouped into a plurality of callcenter groups; determining, for at least some call center groups of theplurality of call center groups, a current interaction metric specificto the call center group; and populating the visualization using thecurrent interaction metric for each of the at least some call centergroups.
 18. The system of claim 11, wherein the operations furthercomprise: querying a data store comprising raw machine data for a callcenter, the raw machine data defining a plurality of interactionsbetween a plurality of call center personnel and a plurality ofcustomers; determining a current interaction metric specific; andpopulating the visualization using the current interaction metric. 19.The system of claim 11, wherein the operations further comprise:obtaining a third party data describing an environment affecting atleast some of a plurality of customers; predicting an effect on the callcenter based on the environment, determining a future interaction metricfor a call center using current interaction data and the effect on thecall center, and populating the visualization using the futureinteraction metric.
 20. The system of claim 11, wherein the operationsfurther comprise: determining an expected volume of calls for a timeperiod, obtaining current interaction data describing a performance of acall center within the time period to a current point in time;predicting, based at least in part on the current interaction data andthe expected volume of calls, a future volume of calls to the callcenter for a remainder of the time period, the remainder of the timeperiod being after the current point in time; determining a currentinteraction metric using the future volume of calls; and displaying thecurrent interaction metric and the expected volume of calls in agraphical user interface.
 21. A non-transitory computer-readable storagemedium storing computer-readable program code which, when executed byone or more processors, cause the one or more processors to performoperations, comprising: obtaining a metric interface hierarchycomprising a plurality of nodes, each node of the plurality of nodesrepresenting an interaction metric and defining a visualization for thenode, the metric interface hierarchy defining an ordering on theplurality of nodes; traversing the metric interface hierarchy startingwith a selected node to obtain a subhierarchy; creating a graphical userinterface from a general interface by, for each node of thesubhierarchy: obtaining an interaction metric for the node, creating thevisualization of the interaction metric a defined for the node, andpopulating the general interface with the visualization of theinteraction metric, wherein the general interface defines a dynamicallyadjustable layout of panes, and wherein populating the general interfacecomprises adding the visualization of the interaction metric to a panematching a location of the node in the ordering defined by the metricinterface hierarchy; and providing the graphical user interface.
 22. Thenon-transitory computer readable medium of claim 21, wherein the generalinterface comprises: a plurality of filter buttons, the plurality offilter buttons for applying a filter function to a calculation ofmetrics defined by at least one node of the subhierarchy.
 23. Thenon-transitory computer readable medium of claim 21, wherein the generalinterface comprises: a second tier for displaying the visualization of aroot node of the subhierarchy, the root node being in the plurality ofnodes; a third tier for displaying the visualization of each directchild node of the root node, the each direct child node being in theplurality of nodes, the third tier disposed under the second tier; and afourth tier for displaying the visualization of each leaf node of thesubhierarchy, the each leaf node being in the plurality of nodes, thefourth tier disposed under the third tier.
 24. The non-transitorycomputer readable medium of claim 21, wherein the subhierarchycomprises: a first node comprising parameters of a graph for servicelevel over time, a second node comprising parameters of a chart forservice level per group, a third node comprising parameters of a chartfor current staffing to target staffing per group, and a fourth nodecomprising parameters of a graph for service level over time per group,wherein the second node, the third node, and the fourth node arechildren nodes of the first node in the subhierarchy.
 25. Thenon-transitory computer readable medium of claim 21, wherein the metricinterface hierarchy comprises: a first node comprising parameters of agraph for call volume, a second node comprising parameters of a chartfor increase in call volume per group, a third node comprisingparameters of a chart for target staffing for call volume per group, anda fourth node comprising parameters of a graph for number of abandonedcalls and processed calls, wherein the second node, the third node, andthe fourth node are children nodes of the first node in the metricinterface hierarchy.
 26. The non-transitory computer readable medium ofclaim 21, wherein the metric interface hierarchy comprises: a first nodecomprising a visualization of calls offered per time period, and asecond node comprising parameters of a chart for calls per time periodper group.
 27. The non-transitory computer readable medium of claim 21,wherein the operations further comprise: querying a data store forcurrent interaction data between a plurality of call center personneland a plurality of customers, the plurality of call center personnelgrouped into a plurality of call center groups; determining, for atleast some call center groups of the plurality of call center groups, acurrent interaction metric specific to the call center group; andpopulating the visualization using the current interaction metric foreach of the at least some call center groups.
 28. The non-transitorycomputer readable medium of claim 21, wherein the operations furthercomprise: querying a data store comprising raw machine data for a callcenter, the raw machine data defining a plurality of interactionsbetween a plurality of call center personnel and a plurality ofcustomers; determining a current interaction metric specific; andpopulating the visualization using the current interaction metric. 29.The non-transitory computer readable medium of claim 21, wherein theoperations further comprise: obtaining a third party data describing anenvironment affecting at least some of a plurality of customers;predicting an effect on the call center based on the environment,determining a future interaction metric for a call center using currentinteraction data and the effect on the call center, and populating thevisualization using the future interaction metric.
 30. Thenon-transitory computer readable medium of claim 21, wherein theoperations further comprise: determining an expected volume of calls fora time period, obtaining current interaction data describing aperformance of a call center within the time period to a current pointin time; predicting, based at least in part on the current interactiondata and the expected volume of calls, a future volume of calls to thecall center for a remainder of the time period, the remainder of thetime period being after the current point in time; determining a currentinteraction metric using the future volume of calls; and displaying thecurrent interaction metric and the expected volume of calls in agraphical user interface.